A distance scale transformation method

Abstract

The application provides a range scale conversion algorithm, and belongs to the technical field of airborne SAR polar format algorithm (PFA) real-time imaging, and specifically comprises the following steps: embedding compensation processing of a fast time offset Delta tau corresponding to each pulse into a matched filter to complete scene center point motion compensation and pulse compression processing of each pulse; and then performing range intercept processing on the pulse compressed data to complete imaging processing of a region of interest near an imaging center, so that the problem of image winding caused by range intercept in the original algorithm can be solved, and the azimuth interpolation processing time and the storage capacity requirement of an imaging system are greatly reduced.

Description

Technical Field

[0001] This application relates to the field of real-time imaging using airborne SAR polar format algorithm (PFA), and in particular to a distance scale transformation method. Background Technology

[0002] The geometric model for airborne spotlight SAR data acquisition is as follows: Figure 1 As shown. The ground imaging area lies in the xy plane, with the imaging center at the origin. The carrier aircraft at speed Flying in a horizontal straight line at an altitude of The oblique angle is The instantaneous coordinates of the radar antenna phase center (APC) are: Point targets in the imaging scene Coordinates are APC to the imaging center and point targets The instantaneous distance is and The radar transmits a linear frequency modulated (LFM) signal, and the demodulated echo signal is as follows:

[0003]

[0004] in, Let t be a rectangular window function, where t is the azimuth time. For azimuth aperture time, Time is fast, The duration of the pulse. At the speed of light, For wavelength, This represents the frequency modulation slope.

[0005] The Polar Format Algorithm (PFA) is a classic spotlight SAR imaging algorithm, widely used due to its simplicity, efficiency, ease of integration with autofocus algorithms, and practical engineering implementation. However, the classic PFA algorithm requires two-dimensional interpolation to convert polar format data to rectangular format data. Since interpolation is performed in the spatial frequency domain, local errors in this domain can lead to an overall degradation in image quality. Therefore, the interpolation algorithm requires high precision, resulting in decreased computational efficiency and limiting the real-time application of PFA. To address the computational burden of interpolation in PFA, Zhu Daiyin et al. introduced the principle of chirp scaling into the PFA algorithm, proposing a range-scale transformation-based PFA algorithm. This algorithm performs range interpolation through a series of FFT and complex multiplication operations, avoiding phase errors caused by insufficient interpolation kernel length and improving the computational efficiency of the PFA algorithm. The range-scale transformation processing flow of the PFA algorithm is as follows:Figure 2 As shown in the figure, Figure 2 In the echo signal, after the complex multiplication of the second phase and the pulse compression, the pulse compression signal at point A is obtained In fast time There is an offset The offset corresponding to different pulses is different, and direct distance intercept processing will cause the SAR image to appear winding in the distance direction, affecting the image quality. SUMMARY

[0006] Therefore, the present application provides a distance scale transformation method, which solves the problems in the prior art and improves the image quality.

[0007] The distance scale transformation method provided by the present application adopts the following technical scheme:

[0008] A distance scale transformation method, for each pulse sampling data, includes:

[0009] Step 1: generate a Scaling function according to the Scaling factor, multiply the Scaling function by each pulse sampling data, and complete the time domain Scaling processing;

[0010] Step 2: convert the result of step 1 into the distance frequency domain;

[0011] Step 3: generate a matching filter based on distance scale transformation, and embed the fast time offset compensation for each pulse into the matching filter;

[0012] Step 4: multiply the result of step 2 by the matching filter, and perform distance IFFT processing to complete pulse compression and fast time offset compensation processing;

[0013] Step 5: according to the system requirement, the result of step 4 is intercepted, and only the interested distance region N1 point is subjected to subsequent imaging processing;

[0014] Step 6: generate an inverse Scaling function and perform compensation processing to obtain an inverse Scaling vector.

[0015] Step 7: convert the result of step 6 into the distance frequency domain;

[0016] Step 8: generate a motion compensation vector and perform motion compensation processing on the result of step 7 to complete the distance scale transformation.

[0017] Optionally, the result of step 1 is subjected to distance FFT processing to convert it into the distance frequency domain.

[0018] Optionally, the matching filter in step 3 is:

[0019] .

[0020] Optionally, the inverse Scaling vector in step six is:

[0021]

[0022] wherein, , , is the number of distance intercept points, is the radar carrier frequency.

[0023] Optionally, the motion compensation filter in step eight is:

[0024]

[0025] wherein, .

[0026] To sum up, the present application includes the following beneficial technical effects:

[0027] The compensation processing of the fast time offset corresponding to each pulse is embedded in the matched filter, the scene center point motion compensation and pulse compression processing of each pulse are completed; the distance intercept processing is performed on the pulse compressed data, the imaging processing of the region of interest near the imaging center is completed, the problem of the SAR image winding caused by the distance intercept in the original algorithm can be solved, and the azimuth interpolation processing time and the storage capacity requirement of the imaging system are greatly reduced. BRIEF DESCRIPTION OF DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0029] Figure 1 It is a spotlight SAR data acquisition geometry model;

[0030] Figure 2 It is a distance scale transformation flowchart for the PFA algorithm;

[0031] Figure 3 It is a distance scale processing flowchart of the present application;

[0032] Figure 4 It is an imaging result of airborne SAR forward-looking data. DETAILED DESCRIPTION

[0033] The embodiments of the present application will be described in detail below with reference to the drawings.

[0034] Following, the embodiments of the present application are described through specific examples, and other advantages and effects of the present application can be easily understood by those skilled in the art from the disclosure. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The present application can also be implemented or applied through other different specific embodiments, and various modifications or changes can be made to the details in the specification without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0035] It should be noted that the various aspects of the embodiments described below are within the scope of the appended claims. It should be apparent that the aspects described herein can be embodied in a wide variety of forms and that any specific structure and / or function described herein is merely illustrative. Based on the teachings herein one skilled in the art should appreciate that an aspect described herein can be implemented independently of any other aspects and that an aspect can be implemented both as any number of software standing on a tangible medium and / or using any number of techniques for software implementation. For example, an aspect can be implemented on a stand-alone computer or can be implemented on a computer network. In addition, an aspect can be implemented as a portion of an operating system or a specific application, library or problem- dependent program.

[0036] It should also be noted that the drawings included in the following description are included for illustrative purposes only and are not intended to limit the scope of the present application. One skilled in the art will be able to make modifications and variations in the embodiments described herein without departing from the scope of the present application.

[0037] In addition, in the following description, specific details are provided to thoroughly understand examples. However, one skilled in the art will understand that the described aspects can be practiced without these specific details.

[0038] In order to solve the problem of large amount of calculation in interpolation processing in PFA algorithm, Zhu Dayin et al. introduced the principle of chirp scaling into PFA algorithm, and proposed PFA algorithm based on distance scale transformation, which realized distance interpolation processing through a series of FFT and complex multiplication operations, not only avoided the phase error caused by insufficient interpolation kernel length, but also improved the operation efficiency of PFA algorithm. The PFA algorithm distance scale transformation processing flow is shown in Figure 2

[0039] Scaling factor:​

[0040]

[0041]

[0042]

[0043]

[0044] wherein, c is the speed of light, is the sampling interval, , is the number of sampling points; is the aperture center elevation angle, is the instantaneous elevation angle corresponding to each pulse, is the instantaneous azimuth angle corresponding to each pulse; , is the sampling frequency.

[0045] When the airborne forward-looking SAR is real-time imaged, the relationship between the radar accumulation points and the parameters such as the resolution, the action distance, and the squint angle is as follows:

[0046]

[0047] wherein, is the azimuth weighting factor, is the wavelength, is the azimuth resolution, and PRF is the pulse repetition frequency.

[0048] When the forward-looking long-range high-resolution real-time imaging is performed, the SAR accumulation points are very large, the storage capacity requirement of the processor is high, and the imaging delay is large.

[0049] When the SAR is imaged, the distance sampling time is as follows:

[0050]

[0051] wherein, is the pulse width, is the distance imaging range. After the distance pulse compression, the distance effective points are corresponding sampling points. After the distance pulse compression, the distance cutting is performed, only the scale transformation and the azimuth interpolation processing are performed on the distance region of interest, the storage requirement of the SAR real-time processing can be greatly reduced, and the SAR real-time signal processing speed is improved. However, Figure 2 , the echo signal is obtained after the complex multiplication twice phase and the pulse compression there is a shift in the fast time ​ If the different offsets corresponding to different pulses are directly subjected to distance intercept processing, the SAR image will be wrapped in the distance direction, affecting the image quality.

[0052] Embodiments of the present application provide a distance scale conversion method.

[0053] As shown in the following figure, a distance scale conversion method includes the following steps for each pulse sampling data: Figure 3

[0054] Step one, generate a Scaling function according to a Scaling factor, multiply the Scaling function by each pulse sampling data, and complete the time domain Scaling processing;

[0055] Step two, convert the result of step one into the distance frequency domain.

[0056] Step three, generate a matched filter based on distance scale conversion, and embed the fast time offset compensation for each pulse into the matched filter.

[0057] Step four, multiply the result of step two by the matched filter, and perform distance IFFT processing to complete pulse compression and fast time offset compensation processing.

[0058] Step five, according to system requirements, intercept the result of step four, and only the interested distance region N1 points are subjected to subsequent imaging processing.

[0059] Step six, generate an inverse Scaling function and perform compensation processing to obtain an inverse Scaling vector.

[0060] Step seven, convert the result of step six into the distance frequency domain; perform distance FFT processing on the result of step six to convert it into the distance frequency domain.

[0061] Step eight, generate a motion compensation vector and perform motion compensation processing on the result of step seven to complete distance scale conversion.

[0062] In one embodiment, a distance scale conversion method includes the following steps for each pulse sampling data:

[0063] Step one, perform distance scale conversion initialization, calculate a Scaling factor according to radar action distance, instantaneous azimuth , instantaneous pitch angle , aperture center pitch angle , and other parameters.

[0064] Step two, calculate a Scaling function according to the Scaling factor, frequency modulation slope, pulse width, imaging center instantaneous slant range, and other parameters ​​and the sampled data Compensation processing is performed to obtain the compensation result. .

[0065] Step 3: Calculate the matched filter based on distance scale transformation using parameters such as scaling factor, frequency modulation slope, sampling rate, and instantaneous slant distance from the imaging center. ,

[0066]

[0067] in, , , The number of intercept points is the distance. This is the radar carrier frequency.

[0068] Step 4, for Perform range FFT and multiply by matched filter The distance IFFT is used to perform distance pulse compression and fast time offset compensation processing, and the required effective N1 points are truncated to obtain the pulse compression post-processing result. .

[0069] Step 5: Calculate the improved inverse scaling function based on parameters such as scaling factor, frequency modulation slope, instantaneous slant distance of the imaging center, pulse width, and carrier frequency. .

[0070]

[0071] in, , , The number of intercept points is the distance. This is the radar carrier frequency.

[0072] Step Six take After completing the inverse scaling compensation process and performing a distance FFT, the following results are obtained: .

[0073] Step 7: Calculate the improved motion compensation function based on parameters such as the scaling factor, instantaneous slant distance of the imaging center, and carrier frequency. .

[0074]

[0075] in, .

[0076] Step 8 take Complete motion compensation processing and distance processing in the PFA algorithm.

[0077] The application can solve the problem of the winding of the SAR image caused by the distance intercepting in the original algorithm by improving the distance scale transformation processing in the PFA algorithm, simultaneously completing the scene center point motion compensation and pulse compression processing for each pulse, and then performing the distance intercepting processing on the pulse compressed data to complete the imaging processing of the region of interest near the imaging center. The application can greatly reduce the distance processing time and the azimuth interpolation processing time in the real-time processing of the PFA algorithm, and simultaneously greatly reduce the requirement of the storage capacity for the imaging algorithm. The application is suitable for the field of the airborne SAR PFA real-time imaging processing.

[0078] The measured data are processed by using the distance scale transformation method, and Table 1 shows the main parameters of a certain airborne SAR imaging system.

[0079] Table 1 Radar imaging parameter table

[0080]

[0081] According to the imaging parameters, the required storage capacity in the real-time imaging processing is shown in Table 2.

[0082] Table 2 Storage capacity comparison table

[0083]

[0084] By using the distance scale method, the required storage capacity is reduced by 4 times after the pulse compression and the distance data intercepting of the system for the subsequent imaging processing. At the same time, since the distance gate number corresponding to the azimuth interpolation processing is reduced by 4 times, the azimuth interpolation time is also reduced by 4 times, which greatly improves the real-time performance of the PFA algorithm.

[0085] Figure 4 (a) The imaging result (horizontal distance, vertical azimuth) of the PFA algorithm based on the scale transformation directly using the distance intercepting has obvious winding phenomenon in the near distance and the far distance, and the imaging effect is poor. Figure 4 (b) The measured data imaging result of the PFA algorithm using the distance scale transformation of the application is shown, and the image focusing effect is good.

[0086] The above is only a specific embodiment of the application, but the protection scope of the application is not limited thereto, and any changes or replacements within the technical range disclosed in the application can be easily thought by those skilled in the art, which should be covered in the protection scope of the application. Therefore, the protection scope of the application should be subject to the protection scope of the claims.

Claims

1. A distance scaling method, wherein data is sampled for each pulse, characterized in that, include: Step 1: Generate a scaling function according to the scaling factor, multiply the sampled data of each pulse by the scaling function, and complete the time-domain scaling process. Step two, convert the results of step one into the distance frequency domain; Step 3: Generate a matched filter based on distance scale transformation, and embed the fast time offset compensation for each pulse into the matched filter; Step 4: Multiply the result of Step 2 by a matched filter and perform distance IFFT processing to complete pulse compression and fast time offset compensation. Step 5: According to system requirements, the results of Step 4 are cropped, and only the distance region N1 of interest is subjected to subsequent imaging processing. Step 6: Generate the inverse Scaling function and perform compensation processing to obtain the inverse Scaling vector; Step 7: Transfer the results from Step 6 into the distance frequency domain; Step 8: Generate motion compensation vectors and perform motion compensation processing on the results of Step 7 to complete the distance scale transformation; The matched filter in step three is: ； The motion compensation filter in step eight is: in, .

2. The distance scale transformation method according to claim 1, characterized in that, Perform distance FFT processing on the result of step one and convert it into the distance frequency domain.

3. The distance scale transformation method according to claim 1, characterized in that, The inverse Scaling vector in step six is: in, , , The number of intercept points is the distance. This is the radar carrier frequency.

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